Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	20,919
2	New Jersey	19,980
3	Louisiana	17,194
4	Arizona	17,024
5	Rhode Island	16,507
6	Massachusetts	16,224
7	District of Columbia	15,453
8	Connecticut	13,325
9	Delaware	13,225
10	Florida	13,149
11	Illinois	12,333
12	Mississippi	12,324
13	Maryland	12,260
14	Alabama	11,328
15	South Carolina	11,297
16	Iowa	11,293
17	Nebraska	11,085
18	Georgia	10,542
19	Arkansas	9,589
20	Utah	9,445
21	Texas	9,439
22	Tennessee	9,315
23	Nevada	9,284
24	California	8,508
25	South Dakota	8,504
26	Virginia	8,393
27	North Carolina	8,366
28	Indiana	7,921
29	Pennsylvania	7,840
30	Michigan	7,745
31	Minnesota	7,590
32	New Mexico	7,292
33	Wisconsin	6,973
34	Kansas	6,955
35	Colorado	6,477
36	Idaho	6,452
37	North Dakota	5,834
38	Ohio	5,719
39	Washington	5,717
40	Oklahoma	5,242
41	Missouri	4,852
42	Kentucky	4,505
43	New Hampshire	4,462
44	Wyoming	3,288
45	Puerto Rico	3,134
46	Oregon	2,948
47	Maine	2,646
48	Alaska	2,519
49	West Virginia	2,406
50	Vermont	2,084
51	Montana	1,785
52	Hawaii	863

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Florida	594
2	Louisiana	372
3	South Carolina	372
4	Alabama	342
5	Arizona	319
6	Nevada	285
7	Texas	273
8	Idaho	271
9	Tennessee	263
10	Georgia	261
11	Arkansas	235
12	Mississippi	230
13	California	208
14	Utah	190
15	North Carolina	188
16	Kansas	167
17	Iowa	147
18	Oklahoma	139
19	North Dakota	125
20	Wisconsin	125
21	Alaska	120
22	Minnesota	118
23	New Mexico	117
24	Ohio	113
25	Washington	107
26	Nebraska	105
27	Virginia	105
28	Montana	98
29	Missouri	93
30	Puerto Rico	91
31	Maryland	89
32	Indiana	87
33	Kentucky	78
34	Delaware	77
35	Oregon	77
36	District of Columbia	76
37	Illinois	75
38	Wyoming	65
39	Colorado	63
40	West Virginia	61
41	Rhode Island	55
42	Michigan	49
43	Pennsylvania	46
44	South Dakota	46
45	Massachusetts	34
46	New Jersey	33
47	New York	33
48	Connecticut	20
49	Hawaii	19
50	New Hampshire	18
51	Maine	14
52	Vermont	12

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,751
2	New York	1,648
3	Connecticut	1,225
4	Massachusetts	1,208
5	Rhode Island	928
6	District of Columbia	804
7	Louisiana	736
8	Michigan	633
9	Illinois	583
10	Maryland	549
11	Pennsylvania	543
12	Delaware	530
13	Mississippi	420
14	Indiana	410
15	Arizona	309
16	Colorado	300
17	New Hampshire	287
18	Georgia	279
19	Minnesota	273
20	Ohio	262
21	New Mexico	261
22	Iowa	239
23	Virginia	230
24	Alabama	229
25	Florida	199
26	Nevada	193
27	South Carolina	188
28	Washington	188
29	Missouri	183
30	California	179
31	Nebraska	154
32	North Carolina	147
33	Kentucky	146
34	Wisconsin	142
35	South Dakota	123
36	North Dakota	119
37	Texas	114
38	Tennessee	108
39	Arkansas	107
40	Oklahoma	107
41	Kansas	103
42	Vermont	89
43	Maine	84
44	Utah	67
45	Idaho	57
46	Oregon	56
47	West Virginia	54
48	Puerto Rico	52
49	Wyoming	36
50	Montana	29
51	Alaska	20
52	Hawaii	14

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Arizona	7
2	Louisiana	3
3	Mississippi	3
4	New Jersey	3
5	Connecticut	2
6	Florida	2
7	Rhode Island	2
8	South Carolina	2
9	Texas	2
10	Alabama	1
11	Arkansas	1
12	California	1
13	Georgia	1
14	Illinois	1
15	Maryland	1
16	Massachusetts	1
17	Michigan	1
18	Missouri	1
19	Montana	1
20	Nevada	1
21	New Mexico	1
22	New York	1
23	North Carolina	1
24	Tennessee	1
25	Alaska	0
26	Colorado	0
27	Delaware	0
28	District of Columbia	0
29	Hawaii	0
30	Idaho	0
31	Indiana	0
32	Iowa	0
33	Kansas	0
34	Kentucky	0
35	Maine	0
36	Minnesota	0
37	Nebraska	0
38	New Hampshire	0
39	North Dakota	0
40	Ohio	0
41	Oklahoma	0
42	Oregon	0
43	Pennsylvania	0
44	Puerto Rico	0
45	South Dakota	0
46	Utah	0
47	Vermont	0
48	Virginia	0
49	Washington	0
50	West Virginia	0
51	Wisconsin	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	133,818	1	99
Lake	Tennessee	99,202	2	99
Lee	Arkansas	93,485	3	99
Dakota	Nebraska	90,832	4	99
Buena Vista	Iowa	88,583	5	99
Davidson	Tennessee	21,180	136	95
Richland	South Carolina	12,014	437	86
Orange	California	7,998	784	75
York	South Carolina	6,936	915	70
Pierce	Washington	4,016	1513	51

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,902	1	99
Randolph	Georgia	3,688	2	99
Terrell	Georgia	3,282	3	99
Early	Georgia	3,042	4	99
Emporia city	Virginia	2,806	5	99
Richland	South Carolina	238	666	78
Davidson	Tennessee	210	731	76
Orange	California	133	999	68
Pierce	Washington	125	1029	67
York	South Carolina	46	1638	47

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.